OpenRaiser/Intern-Atlas huggingface dataset:
Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning
Venue: arXiv cs.CL 2022
Year: 2022

Recent work has shown that NLP tasks such as Relation Extraction (RE) can be recasted as Textual Entailment tasks using verbalizations, with strong performance in zero-shot and few-shot settings thanks to pre-trained entailment models. The fact that relations in current RE datasets are easily verbalized casts doubts on whether entailment would be effective in more complex tasks. In this work we show that entailment is also effective in Event Argument Extraction (EAE), reducing the need of manual annotation to 50% and 20% in ACE and WikiEvents respectively, while achieving the same performance as with full training. More importantly, we show that recasting EAE as entailment alleviates the dependency on schemas, which has been a road-block for transferring annotations between domains. Thanks to the entailment, the multi-source transfer between ACE and WikiEvents further reduces annotation down to 10% and 5% (respectively) of the full training without transfer. Our analysis shows that the key to good results is the use of several entailment datasets to pre-train the entailment model. Similar to previous approaches, our method requires a small amount of effort for manual verbalization: only less than 15 minutes per event argument type is needed, and comparable results can be achieved with users with different level of expertise.
Sampling-free obstacle gradients and reactive planning in Neural Radiance Fields (NeRF)
Venue: arXiv cs.RO 2022
Year: 2022

This work investigates the use of Neural implicit representations, specifically Neural Radiance Fields (NeRF), for geometrical queries and motion planning. We show that by adding the capacity to infer occupancy in a radius to a pre-trained NeRF, we are effectively learning an approximation to a Euclidean Signed Distance Field (ESDF). Using backward differentiation of the augmented network, we obtain an obstacle gradient that is integrated into an obstacle avoidance policy based on the Riemannian Motion Policies (RMP) framework. Thus, our findings allow for very fast sampling-free obstacle avoidance planning in the implicit representation.
Cost-Efficient and QoS-Aware User Association and 3D Placement of 6G Aerial Mobile Access Points
Venue: arXiv cs.NI 2022
Year: 2022

6G networks require a flexible infrastructure to dynamically provide ubiquitous network coverage. Mobile Access Points (MAP) deployment is a promising solution. In this paper, we formulate the joint 3D MAP deployment and user association problem over a dynamic network under interference and mobility constraints. First, we propose an iterative algorithm to optimize the deployment of MAPs. Our solution efficiently and quickly determines the number, position and configuration of MAPs for highly dynamic scenarios. MAPs provide appropriate Quality of Service (QoS) connectivity to mobile ground user in mmwave or sub-6GHz bands and find their optimal positions in a 3D grid. Each MAP also implies an energy cost (e.g. for travel) to be minimized. Once all MAPs deployed, a deep multiagent reinforcement learning algorithm is proposed to associate multiple users to multiple MAPs under interference constraint. Each user acts as an independent agent that operates in a fully distributed architecture and maximizes the network sum-rate.
Publishing a Knowledge Organization System as Linked Data: The Case of the Universal Decimal Classification
Venue: arXiv cs.DL 2022
Year: 2022

Linked data (LD) technology is hailed as a long-awaited solution in web-based information exchange. Linked Open Data (LOD) bring this to another level by enabling meaningful linking of resources and creating a global, openly accessible knowledge graph. Our case is the Universal Decimal Classification (UDC) and the challenges for a KOS service provider to maintain an LD service. UDC was created during the period 1896--1904 to support systematic organization and information retrieval of a bibliography. When discussing UDC as LD we make a distinction between two types of UDC data or two provenances: UDC source data, and UDC codes as they appear in metadata. To serve the purpose of supplying semantics one has to front--end UDC LD with a service that can parse and interpret complex UDC strings. While the use of UDC is free the publishing and distributing of UDC data is protected by a licence. Publishing of UDC both as LD and as LOD must be provided for within a complex service that would allow open access as well as access through a paywall barrier for different levels of licences. The practical task of publishing the UDC as LOD was informed by the '10Things guidelines'. The process includes conceptual parts and technological parts. The transition to a new technology is never a purely mechanical act but is a research endeavour in its own right. The UDC case has shown the importance of cross-domain, inter-disciplinary collaboration which needs experts well situated in multiple knowledge domains.
Neural language models for network configuration: Opportunities and reality check
Venue: arXiv cs.NI 2022
Year: 2022

Boosted by deep learning, natural language processing (NLP) techniques have recently seen spectacular progress, mainly fueled by breakthroughs both in representation learning with word embeddings (e.g. word2vec) as well as novel architectures (e.g. transformers). This success quickly invited researchers to explore the use of NLP techniques to other fields, such as computer programming languages, with the promise to automate tasks in software programming (bug detection, code synthesis, code repair, cross language translation etc.). By extension, NLP has potential for application to network configuration languages as well, for instance considering tasks such as network configuration verification, synthesis, and cross-vendor translation. In this paper, we survey recent advances in deep learning applied to programming languages, for the purpose of code verification, synthesis and translation: in particularly, we review their training requirements and expected performance, and qualitatively assess whether similar techniques can benefit corresponding use-cases in networking.
An Empirical Analysis of the Use of Real-Time Reachability for the Safety Assurance of Autonomous Vehicles
Venue: arXiv cs.RO 2022
Year: 2022

Recent advances in machine learning technologies and sensing have paved the way for the belief that safe, accessible, and convenient autonomous vehicles may be realized in the near future. Despite tremendous advances within this context, fundamental challenges around safety and reliability are limiting their arrival and comprehensive adoption. Autonomous vehicles are often tasked with operating in dynamic and uncertain environments. As a result, they often make use of highly complex components, such as machine learning approaches, to handle the nuances of sensing, actuation, and control. While these methods are highly effective, they are notoriously difficult to assure. Moreover, within uncertain and dynamic environments, design time assurance analyses may not be sufficient to guarantee safety. Thus, it is critical to monitor the correctness of these systems at runtime. One approach for providing runtime assurance of systems with components that may not be amenable to formal analysis is the simplex architecture, where an unverified component is wrapped with a safety controller and a switching logic designed to prevent dangerous behavior. In this paper, we propose using a real-time reachability algorithm for the implementation of the simplex architecture to assure the safety of a 1/10 scale open source autonomous vehicle platform known as F1/10. The reachability algorithm that we leverage (a) provides provable guarantees of safety, and (b) is used to detect potentially unsafe scenarios. In our approach, the need to analyze an underlying controller is abstracted away, instead focusing on the effects of the controller's decisions on the system's future states. We demonstrate the efficacy of our architecture through a vast set of experiments conducted both in simulation and on an embedded hardware platform.
Filtering and Sampling Object-Centric Event Logs
Venue: arXiv cs.DB 2022
Year: 2022

The scalability of process mining techniques is one of the main challenges to tackling the massive amount of event data produced every day in enterprise information systems. To this purpose, filtering and sampling techniques are proposed to keep a subset of the behavior of the original log and make the application of process mining techniques feasible. While techniques for filtering/sampling traditional event logs have been already proposed, filtering/sampling object-centric event logs is more challenging as the number of factors (events, objects, object types) to consider is significantly higher. This paper provides some techniques to filter/sample object-centric event logs.
Computing the jump-term in space-time FEM for arbitrary temporal interpolation
Venue: arXiv cs.CE 2022
Year: 2022

One approach with rising popularity in analyzing time-dependent problems in science and engineering is the so-called space-time finite-element method that utilized finiteelements in both space and time. A common ansatz in this context is to divide the mesh in temporal direction into so-called space-time slabs, which are subsequently weakly connected in time with a Discontinuous Galerkin approach. The corresponding jumpterm, which is responsible for imposing the weak continuity across space-time slabs can be challenging to compute, in particular in the context of deforming domains. Ensuring a conforming discretization of the space-time slab at the top and bottom in time direction simplifies the handling of this term immensely. Otherwise, a computationally expensive and error prone projection of the solution from one time-level to another is necessary. However, when it comes to simulations with deformable domains, e.g. for free-surface flows, ensuring conforming meshes is quite laborious. A possible solution to this challenge is to extrude a spatial mesh in time at each time-step resulting in the so-called time-discontinuous prismatic space-time (D-PST) method. However, this procedure is restricted to finite-elements of 1st order in time. We present a novel algorithmic approach for arbitrarily discretized meshes by flipping the mesh in time-direction for each time-step. This ansatz allows for a simple evaluation of the jump-term as the mesh is always conforming. The cost of flipping the mesh around its symmetry plane in time scales with the number of nodes, which makes it computationally cheaper than an additional update of the mesh to enforce conformity or the evaluation of a projection. We validate the approach on various physical problems with and without deforming domains.
Uniform vs. Non-Uniform Coarse Quantization in Mutual Information Maximizing LDPC Decoding
Venue: arXiv cs.IT 2022
Year: 2022

Recently, low-resolution LDPC decoders have been introduced that perform mutual information maximizing signal processing. However, the optimal quantization in variable and check nodes requires expensive non-uniform operations. Instead, we propose to use uniform quantization with a simple hardware structure, which reduces the complexity of individual node operations approximately by half and shortens the decoding delay significantly. Our analysis shows that the loss of preserved mutual information resulting from restriction to uniform quantization is very small. Furthermore, the error rate simulations with regular LDPC codes confirm that the uniformly quantized decoders cause only minor performance degradation within 0.01 dB compared to the non-uniform alternatives. Due to the complexity reduction, especially the proposed 3-bit decoder is a promising candidate to replace 4-bit conventional decoders.
Privacy Amplification via Random Participation in Federated Learning
Venue: arXiv cs.LG 2022
Year: 2022

Running a randomized algorithm on a subsampled dataset instead of the entire dataset amplifies differential privacy guarantees. In this work, in a federated setting, we consider random participation of the clients in addition to subsampling their local datasets. Since such random participation of the clients creates correlation among the samples of the same client in their subsampling, we analyze the corresponding privacy amplification via non-uniform subsampling. We show that when the size of the local datasets is small, the privacy guarantees via random participation is close to those of the centralized setting, in which the entire dataset is located in a single host and subsampled. On the other hand, when the local datasets are large, observing the output of the algorithm may disclose the identities of the sampled clients with high confidence. Our analysis reveals that, even in this case, privacy guarantees via random participation outperform those via only local subsampling.
Nested Dissection Meets IPMs: Planar Min-Cost Flow in Nearly-Linear Time
Venue: arXiv cs.DS 2022
Year: 2022

We present a nearly-linear time algorithm for finding a minimum-cost flow in planar graphs with polynomially bounded integer costs and capacities. The previous fastest algorithm for this problem is based on interior point methods (IPMs) and works for general sparse graphs in $O(n^{1.5}\text{poly}(\log n))$ time [Daitch-Spielman, STOC'08]. Intuitively, $Ω(n^{1.5})$ is a natural runtime barrier for IPM-based methods, since they require $\sqrt{n}$ iterations, each routing a possibly-dense electrical flow. To break this barrier, we develop a new implicit representation for flows based on generalized nested-dissection [Lipton-Rose-Tarjan, JSTOR'79] and approximate Schur complements [Kyng-Sachdeva, FOCS'16]. This implicit representation permits us to design a data structure to route an electrical flow with sparse demands in roughly $\sqrt{n}$ update time, resulting in a total running time of $O(n\cdot\text{poly}(\log n))$. Our results immediately extend to all families of separable graphs.
SparCAssist: A Model Risk Assessment Assistant Based on Sparse Generated Counterfactuals
Venue: arXiv cs.CL 2022
Year: 2022

We introduce SparcAssist, a general-purpose risk assessment tool for the machine learning models trained for language tasks. It evaluates models' risk by inspecting their behavior on counterfactuals, namely out-of-distribution instances generated based on the given data instance. The counterfactuals are generated by replacing tokens in rational subsequences identified by ExPred, while the replacements are retrieved using HotFlip or Masked-Language-Model-based algorithms. The main purpose of our system is to help the human annotators to assess the model's risk on deployment. The counterfactual instances generated during the assessment are the by-product and can be used to train more robust NLP models in the future.
A Bidirectional Conversion Network for Cross-Spectral Face Recognition
Venue: arXiv cs.CV 2022
Year: 2022

Face recognition in the infrared (IR) band has become an important supplement to visible light face recognition due to its advantages of independent background light, strong penetration, ability of imaging under harsh environments such as nighttime, rain and fog. However, cross-spectral face recognition (i.e., VIS to IR) is very challenging due to the dramatic difference between the visible light and IR imageries as well as the lack of paired training data. This paper proposes a framework of bidirectional cross-spectral conversion (BCSC-GAN) between the heterogeneous face images, and designs an adaptive weighted fusion mechanism based on information fusion theory. The network reduces the cross-spectral recognition problem into an intra-spectral problem, and improves performance by fusing bidirectional information. Specifically, a face identity retaining module (IRM) is introduced with the ability to preserve identity features, and a new composite loss function is designed to overcome the modal differences caused by different spectral characteristics. Two datasets of TINDERS and CASIA were tested, where performance metrics of FID, recognition rate, equal error rate and normalized distance were compared. Results show that our proposed network is superior than other state-of-the-art methods. Additionally, the proposed rule of Self Adaptive Weighted Fusion (SAWF) is better than the recognition results of the unfused case and other traditional fusion rules that are commonly used, which further justifies the effectiveness and superiority of the proposed bidirectional conversion approach.
Practical Saccade Prediction for Head-Mounted Displays: Towards a Comprehensive Model
Venue: arXiv cs.HC 2022
Year: 2022

Eye-tracking technology is an integral component of new display devices such as virtual and augmented reality headsets. Applications of gaze information range from new interaction techniques exploiting eye patterns to gaze-contingent digital content creation. However, system latency is still a significant issue in many of these applications because it breaks the synchronization between the current and measured gaze positions. Consequently, it may lead to unwanted visual artifacts and degradation of user experience. In this work, we focus on foveated rendering applications where the quality of an image is reduced towards the periphery for computational savings. In foveated rendering, the presence of latency leads to delayed updates to the rendered frame, making the quality degradation visible to the user. To address this issue and to combat system latency, recent work proposes to use saccade landing position prediction to extrapolate the gaze information from delayed eye-tracking samples. While the benefits of such a strategy have already been demonstrated, the solutions range from simple and efficient ones, which make several assumptions about the saccadic eye movements, to more complex and costly ones, which use machine learning techniques. Yet, it is unclear to what extent the prediction can benefit from accounting for additional factors. This paper presents a series of experiments investigating the importance of different factors for saccades prediction in common virtual and augmented reality applications. In particular, we investigate the effects of saccade orientation in 3D space and smooth pursuit eye-motion (SPEM) and how their influence compares to the variability across users. We also present a simple yet efficient correction method that adapts the existing saccade prediction methods to handle these factors without performing extensive data collection.
Automated Learning of Interpretable Models with Quantified Uncertainty
Venue: arXiv cs.NE 2022
Year: 2022

Interpretability and uncertainty quantification in machine learning can provide justification for decisions, promote scientific discovery and lead to a better understanding of model behavior. Symbolic regression provides inherently interpretable machine learning, but relatively little work has focused on the use of symbolic regression on noisy data and the accompanying necessity to quantify uncertainty. A new Bayesian framework for genetic-programming-based symbolic regression (GPSR) is introduced that uses model evidence (i.e., marginal likelihood) to formulate replacement probability during the selection phase of evolution. Model parameter uncertainty is automatically quantified, enabling probabilistic predictions with each equation produced by the GPSR algorithm. Model evidence is also quantified in this process, and its use is shown to increase interpretability, improve robustness to noise, and reduce overfitting when compared to a conventional GPSR implementation on both numerical and physical experiments.
Unique Characterisability and Learnability of Temporal Instance Queries
Venue: arXiv cs.LO 2022
Year: 2022

We aim to determine which temporal instance queries can be uniquely characterised by a (polynomial-size) set of positive and negative temporal data examples. We start by considering queries formulated in fragments of propositional linear temporal logic LTL that correspond to conjunctive queries (CQs) or extensions thereof induced by the until operator. Not all of these queries admit polynomial characterisations but by restricting them further to path-shaped queries we identify natural classes that do. We then investigate how far the obtained characterisations can be lifted to temporal knowledge graphs queried by 2D languages combining LTL with concepts in description logics EL or ELI (i.e., tree-shaped CQs). While temporal operators in the scope of description logic constructors can destroy polynomial characterisability, we obtain general transfer results for the case when description logic constructors are within the scope of temporal operators. Finally, we apply our characterisations to establish (polynomial) learnability of temporal instance queries using membership queries in the active learning framework.
Pre-RTL DNN Hardware Evaluator With Fused Layer Support
Venue: arXiv cs.AR 2022
Year: 2022

With the popularity of the deep neural network (DNN), hardware accelerators are demanded for real time execution. However, lengthy design process and fast evolving DNN models make hardware evaluation hard to meet the time to market need. This paper proposes a pre-RTL DNN hardware evaluator that supports conventional layer-by-layer processing as well as the fused layer processing for low external bandwidth requirement. The evaluator supports two state-of-the-art accelerator architectures and finds the best hardware and layer fusion group The experimental results show the layer fusion scheme can achieve 55.6% memory bandwidth reduction, 36.7% latency improvement and 49.2% energy reduction compared with layer-by-layer operation.
Social Practice Cards: Research material to study social contexts as interwoven practice constellations
Venue: arXiv cs.HC 2022
Year: 2022

Studying how social contexts shape technology interactions and how we experience them is hard. One challenge is that social contexts are very dynamic and shaped by the situated practices of everyone involved. As a result, the same human-technology interaction can be experienced quite differently depending on what other people around us do. As a first step to study interpersonal and interpractice dynamics, we collected a broad range of visual representations of practices, such as "riding a bike" or "skipping the rope". This material can be used to further explore how different, co-located practices relate to each other.
A Pre-study on Data Processing Pipelines for Roadside Object Detection Systems Towards Safer Road Infrastructure
Venue: arXiv cs.CV 2022
Year: 2022

Single-vehicle accidents are the most common type of fatal accidents in Sweden, where a car drives off the road and runs into hazardous roadside objects. Proper installation and maintenance of protective objects, such as crash cushions and guard rails, may reduce the chance and severity of such accidents. Moreover, efficient detection and management of hazardous roadside objects also plays an important role in improving road safety. To better understand the state-of-the-art and system requirements, in this pre-study, we investigate the feasibility, implementation, limitations and scaling up of data processing pipelines for roadside object detection. In particular, we divide our investigation into three parts: the target of interest, the sensors of choice and the algorithm design. The data sources we consider in this study cover two common setups: 1) road surveying fleet - annual scans conducted by Trafikverket, the Swedish Transport Administration, and 2) consumer vehicle - data collected using a research vehicle from the laboratory of Resource for vehicle research at Chalmers (REVERE). The goal of this report is to investigate how to implement a scalable roadside object detection system towards safe road infrastructure and Sweden's Vision Zero.